Spring 2019

February 19, 2019

Safeguarding Privacy in Dynamic Decision-Making Problems

Speaker: Kuang Xu (Stanford University)

The increasing ubiquity of large-scale infrastructures for surveillance and data analysis has made understanding the impact of privacy a pressing priority in many domains. We propose a framework for studying a fundamental cost vs. privacy...

February 26, 2019

Coded Computing: A Transformative Framework for Resilient, Secure, and Private Distributed Learning

Speaker: Salman Avestimehr (University of Southern California)

This talk introduces "Coded Computing”, a new framework that brings concepts and tools from information theory and coding into distributed computing to mitigate several performance bottlenecks that arise in large-scale distributed computing...

March 12, 2019

Automatic Computation of Exact Worst-Case Performance for First-Order Methods

Speaker: Julien Hendrickx (UCLouvain)

Joint work with Adrien Taylor (INRIA) and Francois Glineur (UCLouvain). We show that the exact worst-case performances of a wide class of first-order convex optimization algorithms can be obtained as solutions to semi-definite programs,...

April 9, 2019

Personalized Dynamic Pricing with Machine Learning: High Dimensional Covariates and Heterogeneous Elasticity

Speaker: Gah-Yi Ban (London Business School)

We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a $d$-dimensional feature vector. We assume a personalized demand...

April 23, 2019

Memory-Efficient Adaptive Optimization for Humungous-Scale Learning

Speaker: Yoram Singer (Princeton University & Google)

Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each model parameter, thus doubling the memory footprint of the...

April 30, 2019

On Coupling Methods for Nonlinear Filtering and Smoothing

Speaker: Youssef Marzouk (MIT)

Bayesian inference for non-Gaussian state-space models is a ubiquitous problem with applications ranging from geophysical data assimilation to mathematical finance. We will discuss how deterministic couplings between probability...

May 14, 2019

Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery

Speaker: Mihaela van der Schaar (University of Cambridge)

The overarching goal of my research is to develop cutting-edge machine learning, AI and operations research theory, methods, algorithms, and systems to understand the basis of health and disease; develop methodology to catalyze clinical...